class_docstr: Side-Aware Boundary Localization (SABL) for RoI-Head.
  
  Side-Aware features are extracted by conv layers
  with an attention mechanism.
  Boundary Localization with Bucketing and Bucketing Guided Rescoring
  are implemented in BucketingBBoxCoder.
  
  Please refer to https://arxiv.org/abs/1912.04260 for more details.
  
  Args:
      cls_in_channels (int): Input channels of cls RoI feature.             Defaults to 256.
      reg_in_channels (int): Input channels of reg RoI feature.             Defaults to 256.
      roi_feat_size (int): Size of RoI features. Defaults to 7.
      reg_feat_up_ratio (int): Upsample ratio of reg features.             Defaults to 2.
      reg_pre_kernel (int): Kernel of 2D conv layers before             attention pooling. Defaults to 3.
      reg_post_kernel (int): Kernel of 1D conv layers after             attention pooling. Defaults to 3.
      reg_pre_num (int): Number of pre convs. Defaults to 2.
      reg_post_num (int): Number of post convs. Defaults to 1.
      num_classes (int): Number of classes in dataset. Defaults to 80.
      cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
      reg_offset_out_channels (int): Hidden and output channel             of reg offset branch. Defaults to 256.
      reg_cls_out_channels (int): Hidden and output channel             of reg cls branch. Defaults to 256.
      num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
      num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
      reg_class_agnostic (bool): Class agnostic regresion or not.             Defaults to True.
      norm_cfg (dict): Config of norm layers. Defaults to None.
      bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
      loss_cls (dict): Config of classification loss.
      loss_bbox_cls (dict): Config of classification loss for bbox branch.
      loss_bbox_reg (dict): Config of regression loss for bbox branch.
docstr: Extract direction-specific features fx and fy with attention
  methanism.
docstr_tok: ["Extract","direction-specific","features","fx","and","fy","with","attention","methanism","."]
code: def attention_pool(self, reg_x):
      """Extract direction-specific features fx and fy with attention
      methanism."""
      reg_fx = reg_x
      reg_fy = reg_x
      reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
      reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
      reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
      reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
      reg_fx = (reg_fx * reg_fx_att).sum(dim=2)
      reg_fy = (reg_fy * reg_fy_att).sum(dim=3)
      return reg_fx, reg_fy
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
label_desc: Use image node_id 5 for calling the SABLHead obj's underlying member method code with example usage: obj.attention_pool(reg_x) and returns: reg_fx,  reg_fy